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Enhanced Clustering Algorithms For Gray-Scale Image Segmentation

The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. Howeve...

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Main Author: Siddiqui, Fasahat Ullah
Format: Thesis
Language:English
Published: 2012
Subjects:
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf
http://eprints.usm.my/41804/
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id my.usm.eprints.41804
record_format eprints
spelling my.usm.eprints.41804 http://eprints.usm.my/41804/ Enhanced Clustering Algorithms For Gray-Scale Image Segmentation Siddiqui, Fasahat Ullah TK1-9971 Electrical engineering. Electronics. Nuclear engineering The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects). 2012-04 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf Siddiqui, Fasahat Ullah (2012) Enhanced Clustering Algorithms For Gray-Scale Image Segmentation. Masters thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Siddiqui, Fasahat Ullah
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
description The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects).
format Thesis
author Siddiqui, Fasahat Ullah
author_facet Siddiqui, Fasahat Ullah
author_sort Siddiqui, Fasahat Ullah
title Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_short Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_full Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_fullStr Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_full_unstemmed Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
title_sort enhanced clustering algorithms for gray-scale image segmentation
publishDate 2012
url http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf
http://eprints.usm.my/41804/
_version_ 1643710326428925952
score 13.160551

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